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| 2 | "access_rights": "Public", | 2 | "access_rights": "Public", | ||
| 3 | "accrualPeriodicity": "", | 3 | "accrualPeriodicity": "", | ||
| 4 | "author": "Disha Purohit", | 4 | "author": "Disha Purohit", | ||
| 5 | "author_email": "disha.purohit@tib.eu", | 5 | "author_email": "disha.purohit@tib.eu", | ||
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| 10 | "doi": "10.57702/38jfs1vi", | 10 | "doi": "10.57702/38jfs1vi", | ||
| 11 | "doi_date_published": "2024-09-19", | 11 | "doi_date_published": "2024-09-19", | ||
| 12 | "doi_publisher": "TIB", | 12 | "doi_publisher": "TIB", | ||
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| 15 | "end_date": "", | 15 | "end_date": "", | ||
| 16 | "extra_authors": [ | 16 | "extra_authors": [ | ||
| 17 | { | 17 | { | ||
| 18 | "extra_author": "Yashrajsinh Chudasama", | 18 | "extra_author": "Yashrajsinh Chudasama", | ||
| 19 | "orcid": "https://orcid.org/0000-0003-3422-366X" | 19 | "orcid": "https://orcid.org/0000-0003-3422-366X" | ||
| 20 | }, | 20 | }, | ||
| 21 | { | 21 | { | ||
| 22 | "extra_author": "Maria Torrente", | 22 | "extra_author": "Maria Torrente", | ||
| 23 | "orcid": "" | 23 | "orcid": "" | ||
| 24 | }, | 24 | }, | ||
| 25 | { | 25 | { | ||
| 26 | "extra_author": "Maria-Esther Vidal", | 26 | "extra_author": "Maria-Esther Vidal", | ||
| 27 | "orcid": "https://orcid.org/0000-0003-1160-8727" | 27 | "orcid": "https://orcid.org/0000-0003-1160-8727" | ||
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| 44 | "landing_page": "", | 44 | "landing_page": "", | ||
| 45 | "language": "English", | 45 | "language": "English", | ||
| 46 | "license_id": "cc-by", | 46 | "license_id": "cc-by", | ||
| 47 | "license_title": "Creative Commons Attribution", | 47 | "license_title": "Creative Commons Attribution", | ||
| 48 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 48 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
| 49 | "link_orkg": "", | 49 | "link_orkg": "", | ||
| 50 | "maintainer": "Disha Purohit", | 50 | "maintainer": "Disha Purohit", | ||
| 51 | "maintainer_email": "disha.purohit@tib.eu", | 51 | "maintainer_email": "disha.purohit@tib.eu", | ||
| 52 | "metadata_created": "2024-09-19T13:44:11.745534", | 52 | "metadata_created": "2024-09-19T13:44:11.745534", | ||
| 53 | "metadata_modified": "2024-10-01T15:32:00.293121", | 53 | "metadata_modified": "2024-10-01T15:32:00.293121", | ||
| 54 | "name": | 54 | "name": | ||
| 55 | -and-invalidated-symbolic-explanations-for-knowledge-graph-integrity", | 55 | -and-invalidated-symbolic-explanations-for-knowledge-graph-integrity", | ||
| 56 | "notes": "VISE represents a novel hybrid strategy that integrates | 56 | "notes": "VISE represents a novel hybrid strategy that integrates | ||
| 57 | symbolic learning, constraint validation, and numerical learning | 57 | symbolic learning, constraint validation, and numerical learning | ||
| 58 | approaches. VISE employs KGE to capture implicit information and | 58 | approaches. VISE employs KGE to capture implicit information and | ||
| 59 | represent negation in KGs, thereby enhancing the prediction | 59 | represent negation in KGs, thereby enhancing the prediction | ||
| 60 | performance of numerical models. The experimental results demonstrate | 60 | performance of numerical models. The experimental results demonstrate | ||
| 61 | the efficacy of this hybrid technique, which effectively integrates | 61 | the efficacy of this hybrid technique, which effectively integrates | ||
| 62 | the strengths of symbolic, numerical, and constraint validation | 62 | the strengths of symbolic, numerical, and constraint validation | ||
| 63 | paradigms.\r\n\r\nThis collection includes all the data necessary to | 63 | paradigms.\r\n\r\nThis collection includes all the data necessary to | ||
| 64 | reproduce the results from the experimental evaluation of VISE at | 64 | reproduce the results from the experimental evaluation of VISE at | ||
| 65 | EXPLIMED @ ECAI'24.\r\nThe data is an anonymized synthetic lung cancer | 65 | EXPLIMED @ ECAI'24.\r\nThe data is an anonymized synthetic lung cancer | ||
| 66 | benchmark that comprises clinical data extracted from heterogeneous | 66 | benchmark that comprises clinical data extracted from heterogeneous | ||
| 67 | sources such as publications, clinical trials, and clinical records | 67 | sources such as publications, clinical trials, and clinical records | ||
| 68 | representing patients diagnosed with lung cancer. We evaluate the VISE | 68 | representing patients diagnosed with lung cancer. We evaluate the VISE | ||
| 69 | approach on three anonymized Lung Cancer KGs: | 69 | approach on three anonymized Lung Cancer KGs: | ||
| 70 | LC-\ud835\udc3e\ud835\udc3a1, LC-\ud835\udc3e\ud835\udc3a2,and | 70 | LC-\ud835\udc3e\ud835\udc3a1, LC-\ud835\udc3e\ud835\udc3a2,and | ||
| 71 | LC-\ud835\udc3e\ud835\udc3a3\r\n\r\nThe collection comprises nine data | 71 | LC-\ud835\udc3e\ud835\udc3a3\r\n\r\nThe collection comprises nine data | ||
| 72 | sets of three different sizes:\r\n\r\n- LC Knowledge Graph 1 (LC-KG1) | 72 | sets of three different sizes:\r\n\r\n- LC Knowledge Graph 1 (LC-KG1) | ||
| 73 | models 29 lung cancer patients\r\n- LC Knowledge Graph 2 (LC-KG2) | 73 | models 29 lung cancer patients\r\n- LC Knowledge Graph 2 (LC-KG2) | ||
| 74 | models 203 lung cancer patients\r\n- LC Knowledge Graph 3 (LC-KG3) | 74 | models 203 lung cancer patients\r\n- LC Knowledge Graph 3 (LC-KG3) | ||
| 75 | models 319 lung cancer patients\r\n\r\nThree distinct KGs of different | 75 | models 319 lung cancer patients\r\n\r\nThree distinct KGs of different | ||
| 76 | sizes are available, each with its own characteristics. \r\n\r\n- | 76 | sizes are available, each with its own characteristics. \r\n\r\n- | ||
| 77 | \"Original KG\": The original KG comprises of anonymized lung cancer | 77 | \"Original KG\": The original KG comprises of anonymized lung cancer | ||
| 78 | patients with different medical characteristics. \r\n- \"Enriched | 78 | patients with different medical characteristics. \r\n- \"Enriched | ||
| 79 | KG\": Utilizes an inductive learning technique of KG completion | 79 | KG\": Utilizes an inductive learning technique of KG completion | ||
| 80 | through self-supervised symbolic learning over the original KG. \r\n- | 80 | through self-supervised symbolic learning over the original KG. \r\n- | ||
| 81 | \"Transformed KG\": Denotes a transformation of the KG depending on | 81 | \"Transformed KG\": Denotes a transformation of the KG depending on | ||
| 82 | SHACL shapes evaluated across the enriched KGs. This procedure is used | 82 | SHACL shapes evaluated across the enriched KGs. This procedure is used | ||
| 83 | to determine the validity of the data. \r\n\r\nVISE is also evaluated | 83 | to determine the validity of the data. \r\n\r\nVISE is also evaluated | ||
| 84 | with KGs comprising 1242 lung cancer patients (LungCancer-OriginalKG, | 84 | with KGs comprising 1242 lung cancer patients (LungCancer-OriginalKG, | ||
| 85 | LungCancer-EnrichedKG, and LungCancer-TransformedKG).\r\n", | 85 | LungCancer-EnrichedKG, and LungCancer-TransformedKG).\r\n", | ||
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| 87 | "num_tags": 2, | 87 | "num_tags": 2, | ||
| 88 | "orcid": "https://orcid.org/0000-0002-1442-335X", | 88 | "orcid": "https://orcid.org/0000-0002-1442-335X", | ||
| 89 | "organization": { | 89 | "organization": { | ||
| 90 | "approval_status": "approved", | 90 | "approval_status": "approved", | ||
| 91 | "created": "2017-11-23T17:30:37.757128", | 91 | "created": "2017-11-23T17:30:37.757128", | ||
| 92 | "description": "The German National Library of Science and | 92 | "description": "The German National Library of Science and | ||
| 93 | Technology, abbreviated TIB, is the national library of the Federal | 93 | Technology, abbreviated TIB, is the national library of the Federal | ||
| 94 | Republic of Germany for all fields of engineering, technology, and the | 94 | Republic of Germany for all fields of engineering, technology, and the | ||
| 95 | natural sciences.", | 95 | natural sciences.", | ||
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| 194 | "state": "active", | 194 | "state": "active", | ||
| 195 | "tags": [ | 195 | "tags": [ | ||
| 196 | { | 196 | { | ||
| 197 | "display_name": "Knowledge Graph", | 197 | "display_name": "Knowledge Graph", | ||
| 198 | "id": "1bea6e8a-7d3e-45b6-8ebb-3c23ad1b748b", | 198 | "id": "1bea6e8a-7d3e-45b6-8ebb-3c23ad1b748b", | ||
| 199 | "name": "Knowledge Graph", | 199 | "name": "Knowledge Graph", | ||
| 200 | "state": "active", | 200 | "state": "active", | ||
| 201 | "vocabulary_id": null | 201 | "vocabulary_id": null | ||
| 202 | }, | 202 | }, | ||
| 203 | { | 203 | { | ||
| 204 | "display_name": "Symbolic Learning", | 204 | "display_name": "Symbolic Learning", | ||
| 205 | "id": "f9fd23ca-ab6c-482c-b057-06ef41faff5d", | 205 | "id": "f9fd23ca-ab6c-482c-b057-06ef41faff5d", | ||
| 206 | "name": "Symbolic Learning", | 206 | "name": "Symbolic Learning", | ||
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| 209 | } | 209 | } | ||
| 210 | ], | 210 | ], | ||
| 211 | "temporal_resolution": "", | 211 | "temporal_resolution": "", | ||
| 212 | "title": "VISE: Validated and Invalidated Symbolic Explanations for | 212 | "title": "VISE: Validated and Invalidated Symbolic Explanations for | ||
| 213 | Knowledge Graph Integrity", | 213 | Knowledge Graph Integrity", | ||
| 214 | "type": "dataset", | 214 | "type": "dataset", | ||
| 215 | "url": "https://github.com/SDM-TIB/VISE?tab=readme-ov-file", | 215 | "url": "https://github.com/SDM-TIB/VISE?tab=readme-ov-file", | ||
| 216 | "version": "", | 216 | "version": "", | ||
| 217 | "version_note": "" | 217 | "version_note": "" | ||
| 218 | } | 218 | } |